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Task scheduling has vital importance in heterogeneous systems because efficient task scheduling can enhance overall system performance considerably. This paper addresses the task scheduling problem by effective utilization of evolution based algorithm. Genetic algorithms are promising to provide near optimal results even in the large problem space but at the same time the time complexity of Genetic Algorithms are higher. The proposed algorithm, Performance Effective Genetic Algorithm (PEGA) not only provides near optimal schedule but also has a low time complexity. The PEGA efficiently finds the best solution from the search space; PEGA is performance effective due to effective utilization of genetic operators (crossover and mutation) through rigorous search. In addition the chromosome encoding with b-level introduces simplicity with efficiency. The performance is compared through extensive simulations with standard genetic algorithm (SGA). The comparison of results proved that the PEGA outperforms SGA in providing near optimal schedules with considerable less run time.